The past decade has seen a tremendous increase in the adoption of Social Web leading to the generation of enormous amount of user data every day. The constant stream of tweets with an innate complex sentimental and contextual nature makes searching for relevant information a herculean task. Multiple applications use Twitter for various domain sensitive and analytical use-cases. This paper proposes a scalable context modeling framework for a set of tweets for finding two forms of metadata termed as primary and extended contexts. Further, our work presents a hierarchical clustering approach to find hidden patterns by using generated primary and extended contexts. Ontologies from DBpedia are used for generating primary contexts and subsequently to find relevant extended contexts. DBpedia Spotlight in conjunction with DBpedia Ontology forms the backbone for this proposed model. We consider both twitter trend and stream data to demonstrate the application of these contextual parts of information appropriate in clustering. We also discuss the advantages of using hierarchical clustering and information obtained from cutting dendrograms.